Abstract
With the development of robotic technology, au-tonomous robots have been extended to production industries to substitute manual tasks like routine operations. In the general manufacturer, analog gauges are the most commonly utilized and required operators for manual reading. Accordingly, an analog gauge reading can be considered a fundamental feature for the operator robots to be fully automated for inspection purposes. This paper presents the methods for reading multiple analog gauges automatically using a camera. The processing pipeline consists of two main stages: 1) gauge detector for extracting individual gauges and 2) gauge reader for estimating gauge values. For gauge detectors, we propose three different YOLOvS architecture sizes. The gauge readers are mainly categorized into computer-vision approach (CV), and deep learning regression approaches. The deep learning approaches consist of two CNN-based backbones, ResNetSO and EfficientNetV2BO, and one transformer-based SwinTransformer. Finally, we introduce the feasibility of the combination of each gauge detector and reader. As a result, the YOLOv5m detector with EfficientNetV2BO CNN backbone reader theoretically achieves the best performance but is not practical for industrial applications. In contrast, we introduce the YOLOv5m detector with the CV method as the most robust multiple gauge reader. As a result, it reaches the comparative performances to the EfficientNetV2BO backbone and is more compatible with robotic applications.